The EntailmentWriter model is built on top of the text-to-text pretrained T5 transformer [1]},
where the inputs are as described in Section  for Task 1 (no-distractor) and Task 2 (distractor).
For Task 3 (full-corpus), the corpus exceeds T5's token limit, so we add a retrieval step of
30 sentences from the corpus \(C\)  using the hypothesis \(H\)  as query. The retrieved
sentences are then labeled with identifiers and input to the model. This may mean that only a
subset of the required sentences \(S_{gold}\)  is recalled and provided to the model, hence the generated
entailment tree will necessarily be incomplete in such situations, given this strategy.
(Note that other strategies - for future work - could avoid this problem, e.g.,
using multiple retrieval steps).
